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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 465192, 8 pages
http://dx.doi.org/10.1155/2015/465192
Research Article

Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches

1Department of Computer Engineering, Faculty of Computer and Information Science, Sakarya University, 54187 Sakarya, Turkey
2Department of Neurology, Faculty of Medicine, Balikesir University, 10145 Balikesir, Turkey
3Department of Neurology, Faculty of Medicine, Izmir University, 35530 Izmir, Turkey
4Department of Neurology, Faculty of Medicine, Istanbul University, 34093 Istanbul, Turkey

Received 1 January 2015; Accepted 3 April 2015

Academic Editor: Yongqing Yang

Copyright © 2015 Ufuk Çelik et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy.